Ambiguity is uncertainty about probabilities. A decision-maker may know the possible outcomes, but not have one reliable probability distribution describing how likely each outcome is.
Ambiguity Versus Risk
Under risk, probabilities are known or at least treated as known. Under ambiguity, the outcomes may be visible but the probabilities are unclear, disputed, or model-dependent.
That difference matters because standard expected-value or expected-utility reasoning assumes probabilities are available as inputs.
Why Economists Care
Ambiguity helps explain why investors demand extra compensation for unfamiliar assets, why firms delay investment when policy regimes are unclear, and why households may prefer known risks to unknown ones.
A common formal approach is maxmin expected utility, where the decision-maker evaluates an act using the worst expected utility across a set of plausible priors:
[ U(f) = \min_{p \in P} E_p[u(f)] ]
Here P is a set of plausible probability distributions rather than a single one.
Relation To Behavioral Evidence
The Ellsberg paradox is the standard empirical illustration. People often prefer a risky option with known probabilities over an ambiguous option with similar payoffs. That behavior is called ambiguity aversion.